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Partial correlation analysis: Applications for financial markets


  • Dror Y. Kenett
  • Xuqing Huang
  • Irena Vodenska
  • Shlomo Havlin
  • H. Eugene Stanley


The presence of significant cross-correlations between the synchronous time evolution of a pair of equity returns is a well-known empirical fact. The Pearson correlation is commonly used to indicate the level of similarity in the price changes for a given pair of stocks, but it does not measure whether other stocks influence the relationship between them. To explore the influence of a third stock on the relationship between two stocks, we use a partial correlation measurement to determine the underlying relationships between financial assets. Building on previous work, we present a statistically robust approach to extract the underlying relationships between stocks from four different financial markets: the United States, the United Kingdom, Japan, and India. This methodology provides new insights into financial market dynamics and uncovers implicit influences in play between stocks. To demonstrate the capabilities of this methodology, we (i) quantify the influence of different companies and, by studying market similarity across time, present new insights into market structure and market stability, and (ii) we present a practical application, which provides information on the how a company is influenced by different economic sectors, and how the sectors interact with each other. These examples demonstrate the effectiveness of this methodology in uncovering information valuable for a range of individuals, including not only investors and traders but also regulators and policy makers.

Suggested Citation

  • Dror Y. Kenett & Xuqing Huang & Irena Vodenska & Shlomo Havlin & H. Eugene Stanley, 2014. "Partial correlation analysis: Applications for financial markets," Papers 1402.1405,
  • Handle: RePEc:arx:papers:1402.1405

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    References listed on IDEAS

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    4. Brock, W.A. & Hommes, C.H. & Wagener, F.O.O., 2009. "More hedging instruments may destabilize markets," Journal of Economic Dynamics and Control, Elsevier, vol. 33(11), pages 1912-1928, November.
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    6. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
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    Cited by:

    1. repec:eee:phsmap:v:490:y:2018:i:c:p:1309-1323 is not listed on IDEAS
    2. Fernandez, Viviana, 2015. "Influence in commodity markets: Measuring co‐movement globally," Resources Policy, Elsevier, vol. 45(C), pages 151-164.
    3. Jorge A Chan-Lau, 2017. "Variance Decomposition Networks; Potential Pitfalls and a Simple Solution," IMF Working Papers 17/107, International Monetary Fund.

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